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Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia

Journal of Engineering and Technological Sciences · Vol. 55 No. 1 (2023) · hlm. 98–107

DOI
10.5614/j.eng.technol.sci.2023.55.1.10
ISSN
2338-5502, 2337-5779
Terbit
2023-03-31
Penerbit
Directorate for Research and Community Services, Institut Teknologi Bandung
Bahasa
eng

Abstrak

Flood disasters occur frequently in Indonesia and can cause property damage and even death. This research aimed to provide rapid flood mapping based on remote sensing data by using a cloud platform. In this study, the Google Earth Engine cloud platform was used to quickly detect major floods in the Barito watershed in South Kalimantan province, Indonesia. The data used in this study were Sentinel-1 images before and after the flood event, and surface reflectance of Sentinel-2 images available on the Google Earth Engine platform. Flooding is detected using the threshold method. In this study, we determined the threshold using the Otsu method and statistical sampling thresholds (SST). Four SST scenarios were used in this study, combining the mean and standard deviation of the difference backscatter of Sentinel-1 images. The results of this study showed that the second SST scenario could classify floods with the highest accuracy of 73.2%. The inundation area determined by this method was 4,504.33 km2. The first, third and fourth SST scenarios and the Otsu method could reduce the flood load with an overall accuracy of 48.37%, 43.79%, 55.5% and 68.63%, respectively. The SST scenario is considered to be a reasonably good method for rapid flood detection using Sentinel-1 satellite imagery. This rapid detection method can be applied to other areas to detect flooding. This information can be quickly produced to help stakeholders determine appropriate flood management strategies.

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  • National Research and Innovation Agency
  • Indonesian National Institute of Aeronautics and Space
  • University of Indonesia
  • Politeknik Teknologi Nuklir Indonesia
  • National University of Malaysia
  • Bandung Institute of Technology
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Rekan penulisOrganisasiArtikel bersama
Fajar YuliantoNational Research and Innovation Agency7
S SuwarsonoIndonesian National Institute of Aeronautics and Space5
Galdita Aruba ChulafakIndonesian National Institute of Aeronautics and Space5
Sayidah SulmaIndonesian National Institute of Aeronautics and Space3
Nunung Puji NugrohoNational Research and Innovation Agency3
Eddy HermawanIndonesian National Institute of Aeronautics and Space3
Udhi Catur NugrohoIndonesian National Institute of Aeronautics and Space3
Hana Listi FitrianaIndonesian National Institute of Aeronautics and Space3
Eko PriyantoAfiliasi tidak diketahui3
Ahmad FudholiNational University of Malaysia2
Ridwan Arief SubektiUniversity of Indonesia2
Arief SudarmajiUniversity of Indonesia2
Budi PrawaraNational Research and Innovation Agency2
Anjar SusatyoNational Research and Innovation Agency2
Mohammad ArdhaIndonesian National Institute of Aeronautics and Space2
Taufik MaulanaIndonesian National Institute of Aeronautics and Space2
Tinton Dwi AtmajaNational Research and Innovation Agency1
Suwarsono SuwarsonoIndonesian National Institute of Aeronautics and Space1
Atriyon JulzarikaIndonesian National Institute of Aeronautics and Space1
Argo Galih SuhadhaIndonesian National Institute of Aeronautics and Space1
Dipo YudhatamaAfiliasi tidak diketahui1
Rofifatuz Zulfa DarwistaBandung Institute of Technology1
Mardi WibowoNational Research and Innovation Agency1
Maria Noosa AirawatiAfiliasi tidak diketahui1
Hamzah Haru Radityo SuharyantoNational Research and Innovation Agency1
Reno Arief RachmanNational Research and Innovation Agency1
Aloysius Bagyo WidagdoNational Research and Innovation Agency1
Ibnu FauziNational Research and Innovation Agency1
Nofika Cahyani PutriNational Research and Innovation Agency1
Dhedy Husada Fadjar PerdanaNational Research and Innovation Agency1
Jalu Tejo NugrohoIndonesian National Institute of Aeronautics and Space1
Nanik Suryo HaryaniIndonesian National Institute of Aeronautics and Space1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Rapid Flood Mapping Using Statistical Sampling Threshold Based on Sentinel-1 Imagery in the Barito Watershed, South Kalimantan Province, Indonesia</dc:title>
	<dc:creator>Priyatna, Muhammad</dc:creator>
	<dc:creator>Khomarudin, Muhammad Rokhis</dc:creator>
	<dc:creator>Wijaya, Sastra Kusuma</dc:creator>
	<dc:creator>Yulianto, Fajar</dc:creator>
	<dc:creator>Nugroho, Gatot</dc:creator>
	<dc:creator>Afgatiani, Pingkan Mayestika</dc:creator>
	<dc:creator>Rarasati, Anisa</dc:creator>
	<dc:creator>Hussein, Muhammad Arfin</dc:creator>
	<dc:subject xml:lang="en-US">flood</dc:subject>
	<dc:subject xml:lang="en-US">Barito watershed</dc:subject>
	<dc:subject xml:lang="en-US">Sentinel-1</dc:subject>
	<dc:subject xml:lang="en-US">statistical sampling</dc:subject>
	<dc:subject xml:lang="en-US">threshold</dc:subject>
	<dc:subject xml:lang="en-US">Instrumentation Engineering</dc:subject>
	<dc:description xml:lang="en-US">Flood disasters occur frequently in Indonesia and can cause property damage and even death. This research aimed to provide rapid flood mapping based on remote sensing data by using a cloud platform. In this study, the Google Earth Engine cloud platform was used to quickly detect major floods in the Barito watershed in South Kalimantan province, Indonesia. The data used in this study were Sentinel-1 images before and after the flood event, and surface reflectance of Sentinel-2 images available on the Google Earth Engine platform. Flooding is detected using the threshold method. In this study, we determined the threshold using the Otsu method and statistical sampling thresholds (SST). Four SST scenarios were used in this study, combining the mean and standard deviation of the difference backscatter of Sentinel-1 images. The results of this study showed that the second SST scenario could classify floods with the highest accuracy of 73.2%. The inundation area determined by this method was 4,504.33 km2. The first, third and fourth SST scenarios and the Otsu method could reduce the flood load with an overall accuracy of 48.37%, 43.79%, 55.5% and 68.63%, respectively. The SST scenario is considered to be a reasonably good method for rapid flood detection using Sentinel-1 satellite imagery. This rapid detection method can be applied to other areas to detect flooding. This information can be quickly produced to help stakeholders determine appropriate flood management strategies.</dc:description>
	<dc:publisher xml:lang="en-US">Directorate for Research and Community Services, Institut Teknologi Bandung</dc:publisher>
	<dc:date>2023-03-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jets/article/view/19409</dc:identifier>
	<dc:identifier>10.5614/j.eng.technol.sci.2023.55.1.10</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Engineering and Technological Sciences; Vol. 55 No. 1 (2023); 98-107</dc:source>
	<dc:source>2338-5502</dc:source>
	<dc:source>2337-5779</dc:source>
	<dc:source>10.5614/j.eng.technol.sci.2023.55.1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jets/article/view/19409/6140</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of Engineering and Technological Sciences</dc:rights>
</oai_dc:dc>